Economist Martin Wurm joins Inside Economics to discuss the Fed’s rate cut earlier this week, which was larger than the IE team expected. Martin talks about the decision behind the large cut and what it means for the future path of interest rates and the forecasts for the rest of the economy. The team discusses the rate decision’s impact on the housing market and mortgage rates and ends by playing a tough stats game. Guest: Martin Wurm – Director-Economic Research, Moody's Analytics Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X' @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

If you're working on or trying to break into a career in Data Science or Data Engineering, this one is for you. In this episode, Data Engineering expert and recovering Data Scientist Joe Reis shares some of his best tips and strategies for folks looking to launch or accelerate their data careers. You'll leave with practical and actionable advice that you can use to take your career to the next level.   What You'll Learn: Key differences between Analytics, Data Science, and Data Engineering The top skills and tools to focus on for each of these career paths How rapidly changing technology like AI is impacting the future of data jobs   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guest: Joe Reis is a "recovering data scientist" and the co-founder & CEO of Ternary Data. Joe's newest course Fundamentals of Data Engineering Book Follow Joe on LinkedIn

Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

My guest in this episode is Evan Shellshear, an expert in artificial intelligence and co-author of the eye-opening book "Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype." With nearly two decades of experience in developing AI tools, Evan shares his insights into the real challenges and pitfalls of data science projects, and how organizations can overcome these hurdles. About Evan Shellshear: Evan is a renowned AI expert with a Ph.D. in Game Theory from the University of Bielefeld. He has worked globally with leading companies across various industries, using advanced analytics to drive innovation and efficiency. As an author, his work seeks to demystify the complexities of AI and guide organizations toward successful implementation. Episode summary: In this episode, we explore the critical themes of Evan's book, which aims to shed light on why so many data science projects fall short of their potential. We unpack the exaggerated promises and oversimplifications that often lead to these failures, and discuss practical strategies to avoid them. Discussion highlights: Why Do Data Science Projects Fail? Evan discusses the common pitfalls, including unrealistic expectations and lack of understanding of project complexities.Balancing costs and benefits: How organizations can weigh the costs of failure against the potential benefits of successful data science projects.Avoiding failures: Practical advice on increasing success rates by setting realistic goals and aligning projects with business priorities.Impact of organizational culture: How cultural factors within a company can make or break data science initiatives.Measuring success: Effective metrics and indicators for evaluating project outcomes.You can find out more about Evan's book here, and connect with him via LinkedIn.

Our Keynote Panel brings together three Gold Medal Olympians to discuss how they overcame personal challenges and use data to achieve success at the highest levels of sport.

Moderated by Clare Balding, the conversation will delve into how data analytics has transformed their training and competition strategies. They’ll share insights on how data is used across different sports to optimize performance and gain a competitive edge. The discussion will highlight the balance between analytical approaches and the instinctive, experiential aspects of competition.

Attendees will hear inspiring stories of triumph over adversity and gain a deeper understanding of how data is driving success in elite sports today. 

This session offers valuable perspectives on the future of sports analytics and its impact on athletic performance.

This session looks at the ever-increasing demand for data and AI, the current challenges slowing development and how companies can overcome these challenges and shorten time to value using generative AI and open tables like Apache Iceberg. It also looks at how this approach makes it possible to transitioning away from siloed analytical systems to a modern data architecture where multiple teams can create reusable data products across multiple clouds and op-premises environments using generative AI in Data Fabric and share that data across multiple analytical workloads. 

Everything has changed in the last year with Generative AI entering onto the scene. This means a re-shuffling of priorities and budgets, putting AI-enabled Data & Analytics right back at the top of the agenda. In this session we will discuss: 

• That there is no Generative AI without data – but it has to be the right data 

• The importance of being able to bring together organised and trusted data 

• Why your data integration strategy is the foundation to successfully using AI

Other than people, Data is Policing’s biggest asset. With Data being managed differently across Forces how does Policing understand what Data it has and how it is being utilised to support Policing’s objectives? Join Aimee Smith and Kate Boyle to discover how the National Police Data & Analytics Board and PDS successfully delivered Data Maturity assessments across 43 Forces on behalf of the National Police Chief’s Council.

Every day, banking institution Capital on Tap is calculating thousands of credit scores, directly impacting how their customers receive credit cards or additional lines of credit. Data quality is paramount – incorrect credit scores can set off a wide range of long-lasting financial implications for their customers, which is why the team turned to data observability with Monte Carlo, to improve their data – and credit score – reliability. 

But, as with any new tool in your tech stack, onboarding new processes for key users is just as important as onboarding the tool itself. 

Join this session with Ben Jones and Soren Rehn, to hear why the Analytics Engineering team at Capital on Tap decided to invest in a data observability tool, how their processes play a critical role in maximizing the tool’s value (including a few missteps and recalibrations along the way), and the strategies employed to garner widespread success and buy-in over time.

The introduction of Generative AI in the enterprise heralds a new era of advanced analytics and operational efficiency.

The introduction of Generative AI in the enterprise heralds a new era of advanced analytics and operational efficiency. By harnessing the sophisticated capabilities of Gen AI, businesses can significantly accelerate their decision-making processes and empower their employees across multiple dimensions. Gen AI enables intricate data analysis, natural language processing, and decision-making with just a few prompts, facilitating faster innovation and competitive advantage.

However, implementation and optimization of Gen AI for enterprise analytics use cases present several challenges. Gen AI is hard to put into production, due to the complexities associated with data integration and secure data access. Additionally, enterprises struggle to tune and deliver consistently high quality and compelling responses to AI-driven questions.

Join this session to learn how implementing a data fabric can help accelerate time to value and enable Generative AI.

The next big innovation in data management after separation of compute and storage is the open table formats. These formats have truly commoditized storage, allowing you to store data anywhere and run multiple compute workloads without vendor lock-in. This innovation addresses the biggest challenges of cloud data warehousing — performance, usability, and high costs—ushering in the era of the data lakehouse architecture.

In this session, discover how an AI-powered data lakehouse:

• Unlocks data for modern AI use cases

• Enhances performance and enables real-time analytics

• Reduces total cost of ownership (TCO) by up to 75%

• Delivers increased interoperability across the entire data landscape

Join us to explore how the integration of AI with the lakehouse architecture can transform your approach to data management and analytics.

PrimaryBid is on a mission to improve the global IPO market by allowing frictionless participation to more investors than ever before. Central to achieving this mission is the ability to deliver real-time data analytics to a range of audiences. Join us as we discuss our journey to building a best-in-class embedded analytics solution. From dissecting what it means to be best-in-class in 2024, through to identifying constraints and choosing the right technology partners - we?ll provide a how-to, and how-not-to, on creating a premium analytics experience. In an industry where speed, aesthetics, reliability, security, and governance are paramount, discover how we optimize across all dimensions. The session includes a comprehensive overview of our progress to date and a live demonstration showcasing our product in action.

For over three decades we have been powering people and businesses to think and behave differently. In this session, we will share our insights into how you can build the right data culture, by SEEing the value of data through three key pillars: Sponsorship, Education, and Embedding. Detail ? Data helps us to make better, more informed decisions ? and the role of data should not be considered as an ?add-on? to existing capabilities but something that underpins all of those capabilities. Organisations need to understand that this is not just an incremental, evolutionary shift that gives better access to data and richer visualisation ? but something truly transformative when a strong data culture is embedded, alongside elements such as predictive analytics and artificial intelligence. We will discuss with attendees: The importance of adopting a ?shift left? mindset to the use of data in understanding problems, and the designing, developing, testing and operating of solutions. The criticality of investing in culture, which has a disproportionately positive impact on the success of data transformation programmes. The success which can be achieved by following the SEEing model: ~ Sponsorship means demanding better data in order to make better decisions from the Board downwards, and equipping sponsors of business and change programmes to seek and know how to use the right data to deliver better outcomes. ~ Education means helping people understand the value that data can give them; driving demand for data and helping people to see that it should be a foundation in everything they do. ~ Embedding means making data experts integral to teams, in a similar way that DevOps brought operational staff into development teams, which helps to build up understanding and trust between data experts and data users/beneficiaries, increasing domain knowledge in data experts and data/analytics knowledge in the rest of the team. The session will conclude with Q+A.

Join Scott Gamester as he challenges the outdated promises of legacy BI and self-service analytics tools. This session will explore the key issues that have hindered true data-driven decision-making and how modern solutions like Sigma Computing, Databricks, and Snowflake are redefining the landscape. Scott will demonstrate how integrating these platforms empowers business analysts, driving innovation at the edge and enabling AI-enhanced insights. Attendees will learn how these advancements are transforming business empowerment and fostering a new era of creativity and efficiency in analytics.

Imagine what's possible with social media analytics in a world of Generative AI: a whole new level of depth, speed and accuracy in understanding how your customers shop, how they work, how they live, and how they feel about key topics.

In this session, you’ll learn how to capture the context shaping your customers’ environment, emotions and behaviour, and to operationalize this across the enterprise for competitive advantage. Join Quid Founder and President Bob Goodson to discover how the biggest brands in the world are pioneering this new approach.

From its founding in 2023, Skyscanner has leveraged analytical data to optimise business and traveler experiences. And with more than 110 million monthly users resulting in 30+ billion analytical data events per day, Skyscanner is an expert at managing data at scale.

Join Michael Ewins, Director of Engineering at Skyscanner, to learn how his team develops and executes data strategies centered on their core principles of data reliability, trust and rapid data-driven decision making. Michael will dive into the challenges his team faces navigating complex lineage, strategies for effectively combating data incidents, how they simplified their analytics infrastructure for a more practical approach to data governance, and their success in implementing impactful ML and AI business-critical use cases.

Learn how AI is transforming the tech industry. 

Join us for a panel discussion where industry leaders will share insights on using AI to enhance business intelligence, drive innovation, and gain a competitive edge. 

Discover how AI can improve decision-making, personalise customer experiences, and boost operational efficiency. 

This session offers actionable insights for companies looking to harness the power of AI-enhanced analytics. 

Ready to elevate your data game? Join us for an exciting session with Shiv Nayak from EasyJet! Discover how they’re transforming their approach with cutting-edge technology—natural language search, AI, and GPT—to make data insights faster and more accessible than ever. In a fast-paced world where speed and clarity are crucial, see how EasyJet is staying ahead of the competition by turning data into a powerful, self-service tool that drives their success. Shiv will be joined by James Smith, VP EMEA at ThoughtSpot. Don’t miss this chance to learn from the leaders shaping the future of data.

In this insightful fireside chat, Yusen Logistics shares their transformative journey towards embedding a data-driven culture across their organisation, emphasising the pivotal role of analytics in their operational success. The discussion highlights the critical milestones, tools, and strategies that empowered Yusen to evolve from an initial finance automation use case to a company-wide data-driven powerhouse.

Key discussion points include how Yusen:

• Went from a centralised data analytics team to a decentralised model that empowered wider departments.

• Reduced reliance on IT to speed up decision making and scale across the organisation

• Leveraged Alteryx as a catalyst for change, gaining buy in from senior stakeholders and proving value quickly.

In the last decade data has served as a guide to learn from the past, make decisions in the present and the drive insights for the future. The Art of possible that ChatGPT demonstrated in 2023 Channeled investments towards improving data capabilities. Peer competition, emergence of challenger organisations, advance analytics has increased customer expectation and exerted increased pressure on data analysis and exploration . 

These increased expectations has translated into new way of working with data and has demanded teams to be more data driven. This has resulted in emergence of data risk. No matter the expectation there is always a boundary on what data can deliver and cannot deliver. This boundary is directly related to the original intent of data collection and organisational data policies, risk policies and risk appetite. As all part of the organisation touch data and it has become increasingly challenging to mitigate data risks. Acknowledging this major Banks have elevated data risk to Principle risk. This has allowed data office to have more control on how data is being used and accessed within an organisation and most importantly embed business accountability for data as required by most regulations such as BCBS239, GDRP expect. 

In this 30 minutes we will explore 

• What is Data Risk? 
• How to identify Data Risk and design Data Risk Taxonomy? 
• Who are the key stakeholders within an organisation responsible to mitigating data risk? 
• How to design risk appetite for Data Risk? 
• Explore how key data risk controls should look like?